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dc.contributor.authorEqlimi, E
dc.contributor.authorBockstael, A
dc.contributor.authorDe Coensel, B
dc.contributor.authorSchönwiesner, M
dc.contributor.authorTalsma, D
dc.contributor.authorBotteldooren, D
dc.date.accessioned2020-12-07T00:45:34Z
dc.date.available2020-12-07T00:45:34Z
dc.date.issued2020
dc.identifier.issn1664-1078
dc.identifier.doi10.3389/fpsyg.2020.01850
dc.identifier.urihttp://hdl.handle.net/10072/399991
dc.description.abstractHow the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherFrontiers Media SA
dc.relation.ispartofpagefrom1850
dc.relation.ispartofjournalFrontiers in Psychology
dc.relation.ispartofvolume11
dc.subject.fieldofresearchPsychology
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode1701
dc.subject.fieldofresearchcode1702
dc.subject.keywordsEEG
dc.subject.keywordsauditory attention
dc.subject.keywordsauditory perception
dc.subject.keywordsinhibition
dc.subject.keywordslearning context
dc.titleEEG Correlates of Learning From Speech Presented in Environmental Noise
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationEqlimi, E; Bockstael, A; De Coensel, B; Schönwiesner, M; Talsma, D; Botteldooren, D, EEG Correlates of Learning From Speech Presented in Environmental Noise, Frontiers in Psychology, 2020, 11, pp. 1850
dcterms.dateAccepted2020-07-06
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-12-07T00:26:16Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2020 Eqlimi, Bockstael, De Coensel, Schönwiesner, Talsma and Botteldooren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
gro.hasfulltextFull Text
gro.griffith.authorDe Coensel, Bert


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